initial commit

.gitignore

···
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+dataset/

.python-version

···
+3.12

README.md

···
+# NSFW Classifier
+
+This does a few different things:
+
+- Scaffolding for building a ViT classifier
+    - Dataset creation
+    - Model training
+    - Wrapper for existing model, i.e. lets you call `predict_single` or `predict_single__bytes`
+- Firehose consumer (via ATKafka) to scan images with the given model
+- Ozone client to apply moderation labels based on results of the model

config.py

···
+from typing import Dict, List
+from pydantic_settings import BaseSettings, SettingsConfigDict
+import torch
+
+
+class Config(BaseSettings):
+    data_dir: str = "./data"
+    nsfw_dir: str = "./dataset/nsfw"
+    sfw_humans_dir: str = "./dataset/sfw-human"
+    sfw_anime_dir: str = "./dataset/sfw-anime"
+
+    model_save_path: str = "./models/nsfw_vit_classifier"
+
+    model_name: str = "google/vit-base-patch16-224"
+    num_classes: int = 2
+
+    batch_size: int = 32
+    num_epochs: int = 10
+    learning_rate: float = 2e-5
+    weight_decay: float = 0.01
+
+    train_ratio: float = 0.7
+    val_ratio: float = 0.15
+    test_ratio: float = 0.15
+
+    device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    num_workers: int = 4
+
+    class_names: List[str] = ["SFW", "NSFW"]
+    class_to_idx: Dict[str, int] = {"sfw": 0, "nsfw": 1}
+
+    firehose_unsure_output: str = "./firehose-unsure"
+    firehose_sure_output: str = "./firehose-sure"
+
+    kafka_bootstrap_server: str = "10.0.0.66:9092"
+    kafka_topic: str = "atproto-events"
+
+    ozone_pds_url: str = ""
+    ozone_did: str = ""
+    ozone_identifier: str = ""
+    ozone_password: str = ""
+
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+
+CONFIG = Config()

firehose_classifier.py

···
+import asyncio
+import logging
+from pathlib import Path
+from typing import Optional, Tuple
+
+import click
+import httpx
+from atkafka_consumer import AtKafkaEvent, Consumer
+from atproto import models
+
+from config import CONFIG
+from main import NSFWClassifier
+from ozone_client import OzoneClient
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+class FirehoseClassifier:
+    def __init__(self):
+        self.classifier = NSFWClassifier(
+            model_path=CONFIG.model_save_path, device=CONFIG.device
+        )
+        Path(CONFIG.firehose_sure_output).mkdir(exist_ok=True)
+        Path(CONFIG.firehose_unsure_output).mkdir(exist_ok=True)
+
+        self.http_client: Optional[httpx.AsyncClient] = None
+        self.ozone_client: Optional[OzoneClient] = None
+
+        self.sure_threshold = 0.99
+        self.unsure_threshold = 0.925
+
+    async def initialize(self):
+        self.http_client = httpx.AsyncClient(timeout=30.0)
+
+        try:
+            self.ozone_client = await OzoneClient.get_instance(CONFIG)
+            logger.info("Initialized Ozone client")
+        except Exception as e:
+            logger.error(f"Failed to initialize Ozone client: {e}")
+            raise
+
+    async def cleanup(self):
+        if self.http_client:
+            await self.http_client.aclose()
+            logger.info("Closed http client")
+
+        if self.ozone_client:
+            await self.ozone_client.close()
+            logger.info("Closed Ozone client")
+
+    async def on_event(self, evt: AtKafkaEvent):
+        if evt.operation is None:
+            return
+
+        op = evt.operation
+
+        if op.collection != "app.bsky.feed.post":
+            return
+        if op.action != "create":
+            return
+
+        try:
+            post = models.AppBskyFeedPost.Record.model_validate(op.record)
+        except Exception as e:
+            logger.error(f"Failed to validate post record: {e}")
+            return
+
+        if post.embed is None:
+            return
+
+        # skip self-labeled posts
+        if post.labels is not None:
+            for label in post.labels.values:
+                if label.val in ["sexual", "nudity", "porn"]:
+                    logger.info("skipping self-labeled post")
+                    return
+
+        try:
+            images = models.AppBskyEmbedImages.Main.model_validate(post.embed)
+        except Exception:
+            return
+
+        final_label = ""
+        final_pred = 0.0
+
+        for img in images.images:
+            label, pred = await self._process_image(evt.did, str(img.image.cid))
+
+            if label == "likely-nsfw":
+                final_label = label
+                final_pred = pred
+                break
+
+            if label == "possible-nsfw" and final_label == "":
+                final_label = label
+                final_pred = pred
+
+        if final_label and self.ozone_client:
+            post_uri = f"at://{evt.did}/app.bsky.feed.post/{op.rkey}"
+            await self._apply_label(post_uri, op.cid, final_pred, final_label)
+
+    async def _process_image(self, did: str, cid: str) -> Tuple[str, float]:
+        cdn_url = f"https://cdn.bsky.app/img/feed_thumbnail/plain/{did}/{cid}@jpeg"
+
+        try:
+            resp = await self.http_client.get(cdn_url)
+            resp.raise_for_status()
+            image_bytes = resp.content
+        except Exception as e:
+            logger.error(f"Failed to fetch image from {cdn_url}: {e}")
+            return "", 0.0
+
+        try:
+            result = self.classifier.predict_single_bytes(image_bytes)
+            prediction: str = result["prediction"]
+            confidence: float = result["confidence"]
+        except Exception as e:
+            logger.error(f"Failed to classify image {cdn_url}: {e}")
+            return "", 0.0
+
+        if prediction == "NSFW" and confidence >= self.sure_threshold:
+            return "likely-nsfw", confidence
+        elif prediction == "NSFW" and confidence >= self.unsure_threshold:
+            return "possible-nsfw", confidence
+
+        return "", 0.0
+
+    async def _apply_label(self, uri: str, cid: str, pred: float, label: str):
+        try:
+            if label == "likely-nsfw":
+                atproto_label = "porn"
+                comment = f"Automated: Likely NSFW content detected ({pred:.3f})"
+            elif label == "possible-nsfw":
+                atproto_label = "sexual"
+                comment = f"Automated: Possible NSFW content detected ({pred:.3f})"
+            else:
+                return
+
+            await self.ozone_client.add_or_remove_label(
+                action_id=0,
+                entity_id=uri,
+                label=atproto_label,
+                neg=False,
+                comment=comment,
+                cid=cid,
+            )
+
+            logger.info(f"Applied '{atproto_label}' label to {uri}")
+
+        except Exception as e:
+            logger.error(f"Failed to apply label to {uri}: {e}")
+
+
[e-mail]()
+def main():
+    fc = FirehoseClassifier()
+
+    consumer = Consumer(
+        bootstrap_servers=["10.0.0.66:9092"],
+        input_topic="atproto-events",
+        group_id="firehose-classifier",
+        offset="latest",
+        on_event=fc.on_event,
+        max_concurrent_tasks=100,
+    )
+
+    async def run_with_shutdown():
+        loop = asyncio.get_event_loop()
+        import signal
+
+        def handle_signal():
+            logger.info("Received shutdown signal...")
+            asyncio.create_task(consumer.stop())
+
+        for sig in (signal.SIGTERM, signal.SIGINT):
+            loop.add_signal_handler(sig, handle_signal)
+
+        try:
+            logger.info("Initializing firehose client...")
+            await fc.initialize()
+
+            logger.info("Starting Kafka consumer...")
+            await consumer.run()
+
+        except asyncio.CancelledError:
+            logger.info("Consumer cancelled")
+        except Exception as e:
+            logger.error(f"Error in main loop: {e}")
+            raise
+        finally:
+            logger.info("Cleaning up...")
+            await fc.cleanup()
+            await consumer.stop()
+            logger.info("Shutdown complete")
+
+    asyncio.run(run_with_shutdown())
+
+
+if __name__ == "__main__":
+    main()

main.py

···
+import logging
+import random
+from io import BytesIO
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
+
+import click
+import matplotlib.pyplot as plt
+import numpy as np
+import requests
+import seaborn as sns
+import torch.nn as nn
+import torch.optim as optim
+from PIL import Image
+from sklearn.metrics import classification_report, confusion_matrix
+from torch import torch
+from torch.utils.data import DataLoader, Dataset, random_split
+from torchvision import transforms
+from tqdm import tqdm
+from transformers import ViTForImageClassification, ViTImageProcessor
+
+from config import CONFIG, Config
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger()
+
+
+class NSFWDataset(Dataset[torch.Tensor]):
+    def __init__(
+        self,
+        image_paths: List[str],
+        labels: List[int],
+        processor: ViTImageProcessor,
+        augment: bool = False,
+    ):
+        self.image_paths = image_paths
+        self.labels = labels
+        self.processor = processor
+
+        if augment:
+            self.transform = transforms.Compose(
+                [
+                    transforms.RandomHorizontalFlip(p=0.5),
+                    transforms.RandomRotation(15),
+                    transforms.ColorJitter(
+                        brightness=0.2, contrast=0.2, saturation=0.2
+                    ),
+                    transforms.RandomResizedCrop(224, scale=(0.8, 1.0)),
+                ]
+            )
+        else:
+            self.transform = transforms.Compose(
+                [
+                    transforms.Resize((224, 224)),
+                ]
+            )
+
+    def __len__(self) -> int:
+        return len(self.image_paths)
+
+    def __getitem__(self, idx: int) -> Optional[Tuple[torch.Tensor, int]]:
+        img_path = self.image_paths[idx]
+
+        try:
+            image: Image.Image = Image.open(img_path).convert("RGB")
+            image = self.transform(image)
+
+            processed = self.processor(images=image, return_tensors="pt")
+
+            pixel_values = processed["pixel_values"].squeeze(0)
+
+            label: int = self.labels[idx]
+
+            return pixel_values, label
+        except Exception as e:
+            logger.error(f"Error loading image {img_path}: {e}")
+
+
+def load_image_paths_and_labels(
+    nsfw_dir: str,
+    sfw_human_dir: str,
+    sfw_anime_dir: str,
+) -> Tuple[List[str], List[int]]:
+    nsfw_paths: List[str] = []
+    sfw_paths: List[str] = []
+
+    # false positives for nsfw
+    override_paths: List[str] = []
+
+    valid_extensions: Set[str] = {".jpg", ".jpeg", ".png", ".webp"}
+
+    logger.info(f"Loading NSFW images from {nsfw_dir}")
+    nsfw_path = Path(nsfw_dir)
+    if nsfw_path.exists():
+        for f in nsfw_path.iterdir():
+            if f.suffix.lower() in valid_extensions:
+                nsfw_paths.append(str(f))
+    else:
+        logger.warning(f"NSFW directory not found: {nsfw_dir}")
+
+    logger.info(f"Loading SFW human images from {sfw_human_dir}")
+    sfw_humans_path = Path(sfw_human_dir)
+    if sfw_humans_path.exists():
+        for f in sfw_humans_path.iterdir():
+            if f.suffix.lower() in valid_extensions:
+                sfw_paths.append(str(f))
+    else:
+        logger.warning(f"SFW humans directory not found: {sfw_human_dir}")
+
+    logger.info(f"Loading SFW anime images from {sfw_anime_dir}")
+    sfw_anime_path = Path(sfw_anime_dir)
+    if sfw_anime_path.exists():
+        for f in sfw_anime_path.iterdir():
+            if f.suffix.lower() in valid_extensions:
+                sfw_paths.append(str(f))
+    else:
+        logger.warning(f"SFW anime directory not found: {sfw_anime_dir}")
+
+    nsfw_count = len(nsfw_paths)
+    sfw_count = len(sfw_paths)
+
+    logger.info("Loading overrides (NSFW false positives)")
+    override_path = Path("./dataset_clean/overrides")
+    if override_path.exists():
+        for f in override_path.iterdir():
+            if f.suffix.lower() in valid_extensions:
+                override_paths.append(str(f))
+    else:
+        logger.warning("No override path found")
+
+    logger.info(f"Dataset loaded before balancing: {nsfw_count} NSFW, {sfw_count} SFW")
+
+    if nsfw_count > 0 and sfw_count > 0:
+        current_ratio = nsfw_count / sfw_count
+        logger.info(f"Current NSFW/SFW ratio: {current_ratio:.2f}")
+
+        needs_balancing = current_ratio < 0.5 or current_ratio > 2.0
+
+        target_ratio = 1.2
+
+        if needs_balancing:
+            if current_ratio > target_ratio:
+                target_nsfw = int(sfw_count * target_ratio)
+                target_sfw = sfw_count
+                logger.info(f"Downsampling NSFW: {nsfw_count} → {target_nsfw}")
+
+                random.seed(42)
+                nsfw_paths = random.sample(nsfw_paths, target_nsfw)
+            else:
+                target_nsfw = nsfw_count
+                target_sfw = int(nsfw_count / target_ratio)
+                logger.info(f"Downsampling SFW: {sfw_count} → {target_sfw}")
+
+                random.seed(42)
+                sfw_paths = random.sample(sfw_paths, target_sfw)
+
+            nsfw_count = len(nsfw_paths)
+            sfw_count = len(sfw_paths)
+            new_ratio = nsfw_count / sfw_count
+
+            logger.info(f"Dataset after balancing: {nsfw_count} NSFW, {sfw_count} SFW")
+            logger.info(f"New NSFW/SFW ratio: {new_ratio:.2f}")
+
+    image_paths: List[str] = []
+    labels: List[int] = []
+
+    for path in nsfw_paths:
+        image_paths.append(path)
+        labels.append(1)
+
+    for path in sfw_paths:
+        image_paths.append(path)
+        labels.append(0)
+
+    # Add in any overrides so they will show up even after balancing
+    for path in override_paths:
+        image_paths.append(path)
+        labels.append(0)
+
+    combined = list(zip(image_paths, labels))
+    random.shuffle(combined)
+    image_paths, labels = zip(*combined)
+    image_paths = list(image_paths)
+    labels = list(labels)
+
+    logger.info(f"Final dataset: {len(image_paths)} total images")
+
+    return image_paths, labels
+
+
+def create_dataloaders(
+    image_paths: List[str],
+    labels: List[int],
+    processor: ViTImageProcessor,
+    config: Config,
+    augment: bool,
+) -> Tuple[Dataset[torch.Tensor], Dataset[torch.Tensor], Dataset[torch.Tensor]]:
+    total_size = len(image_paths)
+    train_size = int(config.train_ratio * total_size)
+    val_size = int(config.val_ratio * total_size)
+    test_size = total_size - train_size - val_size
+
+    # create our splits without actually augmenting anything
+    full_dataset = NSFWDataset(image_paths, labels, processor, augment=False)
+
+    train_dataset_temp, val_dataset_temp, test_dataset_temp = random_split(
+        full_dataset,
+        [train_size, val_size, test_size],
+        generator=torch.Generator().manual_seed(42),
+    )
+
+    train_indicies = train_dataset_temp.indices
+    val_indicies = val_dataset_temp.indices
+    test_indicies = test_dataset_temp.indices
+
+    train_paths = [image_paths[i] for i in train_indicies]
+    train_labels = [labels[i] for i in train_indicies]
+    train_dataset = NSFWDataset(train_paths, train_labels, processor, augment)
+
+    val_paths = [image_paths[i] for i in val_indicies]
+    val_labels = [labels[i] for i in val_indicies]
+    val_dataset = NSFWDataset(val_paths, val_labels, processor, augment=False)
+
+    test_paths = [image_paths[i] for i in test_indicies]
+    test_labels = [labels[i] for i in test_indicies]
+    test_dataset = NSFWDataset(test_paths, test_labels, processor, augment=False)
+
+    pin_memory = config.device.type == "cuda"
+
+    train_loader = DataLoader[torch.Tensor](
+        train_dataset,
+        batch_size=config.batch_size,
+        shuffle=True,
+        num_workers=config.num_workers,
+        pin_memory=pin_memory,
+    )
+
+    val_loader = DataLoader[torch.Tensor](
+        val_dataset,
+        batch_size=config.batch_size,
+        shuffle=False,
+        num_workers=config.num_workers,
+        pin_memory=pin_memory,
+    )
+
+    test_loader = DataLoader[torch.Tensor](
+        test_dataset,
+        batch_size=config.batch_size,
+        shuffle=False,
+        num_workers=config.num_workers,
+        pin_memory=pin_memory,
+    )
+
+    return train_loader, val_loader, test_loader
+
+
+def create_model(config: Config) -> ViTForImageClassification:
+    logger.info(f"Loading pretrained ViT model: {config.model_name}")
+
+    model = ViTForImageClassification.from_pretrained(
+        config.model_name,
+        num_labels=config.num_classes,
+        ignore_mismatched_sizes=True,
+    )
+
+    model = model.to(config.device)
+
+    return model
+
+
+def train_epoch(
+    model: ViTForImageClassification,
+    train_loader: DataLoader[torch.Tensor],
+    criterion: nn.Module,
+    optimizer: optim.Optimizer,
+    device: torch.device,
+):
+    model.train()
+    running_loss: float = 0.0
+    correct: int = 0
+    total: int = 0
+
+    progress_bar = tqdm(train_loader, desc="Training")
+
+    for batch_idx, (images, labels) in enumerate(progress_bar):
+        images: torch.Tensor = images.to(device)
+        labels: torch.Tensor = labels.to(device)
+
+        optimizer.zero_grad()
+
+        outputs = model(pixel_values=images)
+        logits: torch.Tensor = outputs.logits
+
+        loss: torch.Tensor = criterion(logits, labels)
+
+        loss.backward()
+        optimizer.step()
+
+        running_loss += loss.item()
+        _, predicted = torch.max(logits, 1)
+        total += labels.size(0)
+        correct += int((predicted == labels).sum().item())
+
+        progress_bar.set_postfix(
+            {"loss": running_loss / (batch_idx + 1), "acc": 100.0 * correct / total}
+        )
+
+    epoch_loss: float = running_loss / len(train_loader)
+    epoch_acc: float = 100.0 * correct / total
+
+    return epoch_loss, epoch_acc
+
+
+def validate(
+    model: ViTForImageClassification,
+    val_loader: DataLoader[torch.Tensor],
+    criterion: nn.Module,
+    device: torch.device,
+):
+    model.eval()
+    running_loss: float = 0.0
+    correct: int = 0
+    total: int = 0
+
+    all_preds: List[int] = []
+    all_labels: List[int] = []
+
+    with torch.no_grad():
+        progress_bar = tqdm(val_loader, desc="Validation")
+
+        for images, labels in progress_bar:
+            images: torch.Tensor = images.to(device)
+            labels: torch.Tensor = labels.to(device)
+
+            outputs = model(pixel_values=images)
+            logits: torch.Tensor = outputs.logits
+
+            loss: torch.Tensor = criterion(logits, labels)
+
+            running_loss += loss.item()
+            _, predicted = torch.max(logits, 1)
+            total += labels.size(0)
+            correct += int((predicted == labels).sum().item())
+
+            all_preds.extend(predicted.cpu().numpy().tolist())
+            all_labels.extend(labels.cpu().numpy().tolist())
+
+            progress_bar.set_postfix(
+                {"loss": running_loss / len(val_loader), "acc": 100.0 * correct / total}
+            )
+
+    epoch_loss: float = running_loss / len(val_loader)
+    epoch_acc: float = 100.0 * correct / total
+
+    return epoch_loss, epoch_acc, all_preds, all_labels
+
+
+def train_model(
+    model: ViTForImageClassification,
+    train_loader: DataLoader[torch.Tensor],
+    val_loader: DataLoader[torch.Tensor],
+    config: Config,
+):
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.AdamW(
+        model.parameters(),
+        lr=config.learning_rate,
+        weight_decay=config.weight_decay,
+    )
+    scheduler = optim.lr_scheduler.ReduceLROnPlateau(
+        optimizer,
+        mode="min",
+        factor=0.5,
+        patience=2,
+    )
+
+    best_val_acc = 0.0
+    best_model_state: Optional[Dict[str, Any]] = None
+
+    history: Dict[str, List[float]] = {
+        "train_loss": [],
+        "train_acc": [],
+        "val_loss": [],
+        "val_acc": [],
+    }
+
+    for epoch in range(config.num_epochs):
+        logger.info(f"Epoch {epoch + 1}/{config.num_epochs}")
+        print("-" * 60)
+
+        train_loss, train_acc = train_epoch(
+            model,
+            train_loader,
+            criterion,
+            optimizer,
+            config.device,
+        )
+
+        val_loss, val_acc, _, _ = validate(model, val_loader, criterion, config.device)
+
+        scheduler.step(val_loss)
+
+        history["train_loss"].append(train_loss)
+        history["train_acc"].append(train_acc)
+        history["val_loss"].append(val_loss)
+        history["val_acc"].append(val_acc)
+
+        print(f"\nEpoch {epoch + 1} Summary:")
+        print(f"Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.2f}")
+        print(f"Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.2f}")
+
+        if val_acc > best_val_acc:
+            best_val_acc = val_acc
+            best_model_state = model.state_dict().copy()
+            print(f"New best model! (Val Acc: {val_acc:2f}%)")
+
+    if best_model_state is not None:
+        model.load_state_dict(best_model_state)
+
+    logger.info(f"Training complete. Best validation accuracy: {best_val_acc:.2f}%")
+
+    return model, history
+
+
+def save_model(
+    model: ViTForImageClassification,
+    processor: ViTImageProcessor,
+    config: Config,
+):
+    Path(config.model_save_path).mkdir(parents=True, exist_ok=True)
+
+    model.save_pretrained(config.model_save_path)
+    processor.save_pretrained(config.model_save_path)
+
+    print(f"Model saved to {config.model_save_path}")
+
+
+def load_trained_model(
+    model_path: str, device: torch.device
+) -> Tuple[ViTForImageClassification, ViTImageProcessor]:
+    model = ViTForImageClassification.from_pretrained(model_path)
+    processor = ViTImageProcessor.from_pretrained(model_path)
+    model = model.to(device)
+    model.eval()
+    return model, processor
+
+
+def predict_image(
+    image_path: str,
+    model: ViTForImageClassification,
+    processor: ViTImageProcessor,
+    device: torch.device,
+    config: Config,
+) -> Tuple[str, float]:
+    model.eval()
+
+    image: Image.Image = Image.open(image_path).convert("RGB")
+    inputs: Dict[str, torch.Tensor] = processor(images=image, return_tensors="pt")
+    pixel_values: torch.Tensor = inputs["pixel_values"].to(device)
+
+    with torch.no_grad():
+        outputs = model(pixel_values=pixel_values)
+        logits: torch.Tensor = outputs.logits
+        probs: torch.Tensor = torch.softmax(logits, dim=1)
+        predicted_class: int = torch.argmax(probs, dim=1).item()
+        confidence: float = probs[0, predicted_class].item()
+
+    prediction: str = config.class_names[predicted_class]
+
+    return prediction, confidence
+
+
+def evaluate_model(
+    model: ViTForImageClassification,
+    test_loader: DataLoader[torch.Tensor],
+    config: Config,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    model.eval()
+    all_preds: List[int] = []
+    all_labels: List[int] = []
+    all_probs: List[List[float]] = []
+
+    print("\n" + "=" * 60)
+    print("Evaluating on Test Set")
+    print("=" * 60)
+
+    with torch.no_grad():
+        for images, labels in tqdm(test_loader, desc="Testing"):
+            images: torch.Tensor = images.to(config.device)
+
+            outputs = model(pixel_values=images)
+            logits: torch.Tensor = outputs.logits
+
+            probs: torch.Tensor = torch.softmax(logits, dim=1)
+            _, predicted = torch.max(logits, 1)
+
+            all_preds.extend(predicted.cpu().numpy().tolist())
+            all_labels.extend(labels.numpy().tolist())
+            all_probs.extend(probs.cpu().numpy().tolist())
+
+    all_preds_array: np.ndarray = np.array(all_preds)
+    all_labels_array: np.ndarray = np.array(all_labels)
+    all_probs_array: np.ndarray = np.array(all_probs)
+
+    print("\nClassification Report:")
+    print(
+        classification_report(
+            all_labels_array, all_preds_array, target_names=config.class_names, digits=4
+        )
+    )
+
+    cm: np.ndarray = confusion_matrix(all_labels_array, all_preds_array)
+
+    plt.figure(figsize=(8, 6))
+    sns.heatmap(
+        cm,
+        annot=True,
+        fmt="d",
+        cmap="Blues",
+        xticklabels=config.class_names,
+        yticklabels=config.class_names,
+    )
+    plt.title("Confusion Matrix")
+    plt.ylabel("True Label")
+    plt.xlabel("Predicted Label")
+    plt.tight_layout()
+    plt.savefig("confusion_matrix.png", dpi=300, bbox_inches="tight")
+    print("\n✓ Confusion matrix saved as 'confusion_matrix.png'")
+
+    return all_preds_array, all_labels_array, all_probs_array
+
+
+def plot_training_history(history: Dict[str, List[float]]) -> None:
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
+
+    ax1.plot(history["train_loss"], label="Train Loss", marker="o")
+    ax1.plot(history["val_loss"], label="Val Loss", marker="s")
+    ax1.set_xlabel("Epoch")
+    ax1.set_ylabel("Loss")
+    ax1.set_title("Training and Validation Loss")
+    ax1.legend()
+    ax1.grid(True)
+
+    ax2.plot(history["train_acc"], label="Train Acc", marker="o")
+    ax2.plot(history["val_acc"], label="Val Acc", marker="s")
+    ax2.set_xlabel("Epoch")
+    ax2.set_ylabel("Accuracy (%)")
+    ax2.set_title("Training and Validation Accuracy")
+    ax2.legend()
+    ax2.grid(True)
+
+    plt.tight_layout()
+    plt.savefig("training_history.png", dpi=300, bbox_inches="tight")
+    print("✓ Training history saved as 'training_history.png'")
+
+
+class NSFWClassifier:
+    def __init__(self, model_path: str, device: Optional[torch.device] = None) -> None:
+        if device is None:
+            self.device: torch.device = torch.device(
+                "cuda" if torch.cuda.is_available() else "cpu"
+            )
+        else:
+            self.device = device
+
+        print(f"Loading model from {model_path}...")
+        self.model: ViTForImageClassification = (
+            ViTForImageClassification.from_pretrained(model_path)
+        )
+        self.processor: ViTImageProcessor = ViTImageProcessor.from_pretrained(
+            model_path
+        )
+        self.model = self.model.to(self.device)
+        self.model.eval()
+
+        self.class_names: List[str] = ["SFW", "NSFW"]
+        print(f"✓ Model loaded on {self.device}")
+
+    def predict_single(self, image_path: str) -> Dict[str, Union[str, float]]:
+        image: Image.Image = Image.open(image_path).convert("RGB")
+
+        inputs: Dict[str, torch.Tensor] = self.processor(
+            images=image, return_tensors="pt"
+        )
+        pixel_values: torch.Tensor = inputs["pixel_values"].to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(pixel_values=pixel_values)
+            logits: torch.Tensor = outputs.logits
+            probs: torch.Tensor = torch.softmax(logits, dim=1)[0]
+            predicted_class: int = torch.argmax(probs).item()
+
+        return {
+            "prediction": self.class_names[predicted_class],
+            "confidence": probs[predicted_class].item(),
+            "sfw_prob": probs[0].item(),
+            "nsfw_prob": probs[1].item(),
+        }
+
+    def predict_single_bytes(self, image_bytes: bytes) -> Dict[str, Union[str, float]]:
+        image = Image.open(BytesIO(image_bytes)).convert("RGB")
+        inputs: Dict[str, torch.Tensor] = self.processor(
+            images=image, return_tensors="pt"
+        )
+        pixel_values: torch.Tensor = inputs["pixel_values"].to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(pixel_values=pixel_values)
+            logits: torch.Tensor = outputs.logits
+            probs: torch.Tensor = torch.softmax(logits, dim=1)[0]
+            predicted_class: int = torch.argmax(probs).item()
+
+        return {
+            "prediction": self.class_names[predicted_class],
+            "confidence": probs[predicted_class].item(),
+            "sfw_prob": probs[0].item(),
+            "nsfw_prob": probs[1].item(),
+        }
+
+
[e-mail]()
+def cli():
+    pass
+
+
[e-mail]()
[e-mail](
+    "--augment",
+    type=bool,
+    default=False,
+    required=False,
+)
+def train(
+    augment: Optional[bool],
+):
+    config = CONFIG
+
+    print("=" * 60)
+    print("NSFW Image Classifier - ViT Finetuner")
+    print("=" * 60)
+    print(f"Device: {config.device}")
+    print(f"Model: {config.model_name}")
+    print(f"Batch size: {config.batch_size}")
+    print(f"Learning rate: {config.learning_rate}")
+    print(f"Epochs: {config.num_epochs}")
+
+    logger.info("Loading image processor")
+    processor = ViTImageProcessor.from_pretrained(config.model_name)
+
+    logger.info("Creating dataset")
+    image_paths, labels = load_image_paths_and_labels(
+        config.nsfw_dir,
+        config.sfw_humans_dir,
+        config.sfw_anime_dir,
+    )
+
+    logger.info("Creating dataloaders")
+    train_loader, val_loader, test_loader = create_dataloaders(
+        image_paths, labels, processor, config, augment=augment or False
+    )
+
+    model = create_model(config)
+
+    model, history = train_model(model, train_loader, val_loader, config)
+
+    logger.info("Evaluating model")
+    evaluate_model(model, test_loader, config)
+
+    logger.info("Generating plots")
+    plot_training_history(history)
+
+    logger.info("Saving model")
+    save_model(model, processor, config)
+
+
[e-mail]()
[e-mail]("image", type=str, required=True)
+def predict(
+    image: str,
+):
+    classifer = NSFWClassifier(
+        CONFIG.model_save_path,
+        CONFIG.device,
+    )
+
+    print(f"{classifer.predict_single(image)}")
+
+
[e-mail]()
[e-mail]("url", type=str, required=True)
+def predict_url(
+    url: str,
+):
+    classifier = NSFWClassifier(
+        CONFIG.model_save_path,
+        CONFIG.device,
+    )
+
+    resp = requests.get(url)
+    resp.raise_for_status()
+
+    # we can use predict_bytes_single, but we wont so that we can do something with this image like
+    # move it to a directory for retraining
+    with open("./testimg.jpeg", "wb") as f:
+        f.write(resp.content)
+
+    print(f"{classifier.predict_single('./testimg.jpeg')}")
+
+
+if __name__ == "__main__":
+    cli()

ozone_client.py

···
+import asyncio
+import logging
+from datetime import datetime, timedelta
+from typing import Any, Dict, Optional
+
+import httpx
+
+from config import Config
+
+logger = logging.getLogger(__name__)
+
+
+class OzoneClient:
+    """
+    An Ozone client that I took out of Osprey, but uses asyncio instead of gevent for
+    concurrency. In particular, it will manage a session and keep it refreshed. Removed
+    some of the features like repo fetching since we don't need them here.
+    """
+
+    _instance: Optional["OzoneClient"] = None
+    _init_lock = asyncio.Lock()
+
+    def __init__(self, config: Config):
+        self._session_lock = asyncio.Lock()
+
+        self._access_jwt: Optional[str] = None
+        self._refresh_jwt: Optional[str] = None
+
+        self._last_refresh: Optional[datetime] = None
+        self._refresh_interval = timedelta(minutes=30)
+
+        self._did = config.ozone_did
+        self._identifier = config.ozone_identifier
+        self._password = config.ozone_password
+        self._pds_url = config.ozone_pds_url
+        self._request_timeout = 30.0
+
+        self._http_client: Optional[httpx.AsyncClient] = None
+
+        if not self._identifier or not self._password:
+            raise ValueError("OZONE_IDENTIFIER and OZONE_PASSWORD must be set")
+
+    async def _ensure_http_client(self) -> httpx.AsyncClient:
+        if self._http_client is None:
+            self._http_client = httpx.AsyncClient(timeout=self._request_timeout)
+        return self._http_client
+
+    @classmethod
+    async def get_instance(cls, config: Config) -> "OzoneClient":
+        if cls._instance is None:
+            async with cls._init_lock:
+                if cls._instance is None:
+                    cls._instance = cls(config)
+                    await cls._instance._create_session()
+        return cls._instance
+
+    async def _create_session(self):
+        async with self._session_lock:
+            try:
+                client = await self._ensure_http_client()
+                resp = await client.post(
+                    f"{self._pds_url}/xrpc/com.atproto.server.createSession",
+                    json={
+                        "identifier": self._identifier,
+                        "password": self._password,
+                    },
+                )
+                resp.raise_for_status()
+                data = resp.json()
+
+                self._access_jwt = data["accessJwt"]
+                self._refresh_jwt = data["refreshJwt"]
+                self._did = data["did"]
+                self._last_refresh = datetime.now()
+
+                logger.info(f"Created Bluesky session for DID: {self._did}")
+            except Exception as e:
+                logger.error(f"Failed to create Bluesky session: {e}")
+                raise
+
+    async def _refresh_session(self):
+        async with self._session_lock:
+            try:
+                client = await self._ensure_http_client()
+                resp = await client.post(
+                    f"{self._pds_url}/xrpc/com.atproto.server.refreshSession",
+                    headers={"authorization": f"Bearer {self._refresh_jwt}"},
+                )
+                resp.raise_for_status()
+                data = resp.json()
+
+                self._access_jwt = data["accessJwt"]
+                self._refresh_jwt = data["refreshJwt"]
+                self._last_refresh = datetime.now()
+
+                logger.debug(f"Refreshed Bluesky session for DID: {self._did}")
+            except Exception as e:
+                logger.error(f"Failed to refresh session: {e}")
+                logger.info("Attempting to create new session after refresh failure")
+                await self._create_session()
+
+    async def _ensure_valid_session(self):
+        if (
+            self._last_refresh is None
+            or datetime.now() - self._last_refresh >= self._refresh_interval
+        ):
+            await self._refresh_session()
+
+    async def _get_auth_headers(self) -> Dict[str, str]:
+        await self._ensure_valid_session()
+
+        return {
+            "accept": "*/*",
+            "content-type": "application/json",
+            "authorization": f"Bearer {self._access_jwt}",
+        }
+
+    async def _get_moderation_headers(self) -> Dict[str, str]:
+        await self._ensure_valid_session()
+
+        labeler_did = self._did
+
+        return {
+            "accept": "*/*",
+            "content-type": "application/json",
+            "authorization": f"Bearer {self._access_jwt}",
+            "atproto-accept-labelers": f"{labeler_did};redact",
+            "atproto-proxy": f"{labeler_did}#atproto_labeler",
+        }
+
+    def get_did(self) -> Optional[str]:
+        return self._did
+
+    def get_pds_url(self) -> str:
+        return self._pds_url
+
+    async def add_or_remove_label(
+        self,
+        action_id: int,
+        entity_id: str,
+        label: str,
+        neg: bool,
+        comment: Optional[str] = None,
+        expiration_in_hours: Optional[int] = None,
+        cid: Optional[str] = None,
+    ):
+        try:
+            subject: Dict[str, str] = {}
+            if entity_id.startswith("did:"):
+                subject["$type"] = "com.atproto.admin.defs#repoRef"
+                subject["did"] = entity_id
+            elif entity_id.startswith("at://"):
+                subject["$type"] = "com.atproto.repo.strongRef"
+                subject["cid"] = cid or ""
+                subject["uri"] = entity_id
+            else:
+                raise ValueError(f"Invalid entity_id format: {entity_id}")
+
+            payload: Dict[str, Any] = {
+                "subject": subject,
+                "createdBy": self._did,
+                "subjectBlobCids": [],
+                "event": {
+                    "$type": "tools.ozone.moderation.defs#modEventLabel",
+                    "comment": comment or "",
+                    "createLabelVals": [label] if not neg else [],
+                    "negateLabelVals": [label] if neg else [],
+                    "durationInHours": expiration_in_hours or 0,
+                },
+                "modTool": {"name": "osprey", "meta": {"actionId": str(action_id)}},
+            }
+
+            headers = await self._get_moderation_headers()
+            client = await self._ensure_http_client()
+
+            resp = await client.post(
+                f"{self._pds_url}/xrpc/tools.ozone.moderation.emitEvent",
+                headers=headers,
+                json=payload,
+            )
+            resp.raise_for_status()
+
+            action = "removed" if neg else "added"
+            logger.info(f"Successfully {action} label '{label}' for {entity_id}")
+
+        except Exception as e:
+            logger.error(f"Failed to emit label event for {entity_id}: {e}")
+            raise
+
+    async def close(self):
+        """Close the HTTP client. Call this when shutting down."""
+        if self._http_client is not None:
+            await self._http_client.aclose()
+            self._http_client = None
+            logger.info("Closed OzoneClient HTTP connection")

pyproject.toml

···
+[project]
+name = "pclassifier"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "atkafka-consumer-python",
+    "atproto>=0.0.65",
+    "click>=8.3.1",
+    "httpx>=0.28.1",
+    "matplotlib>=3.10.8",
+    "numpy>=2.4.0",
+    "pydantic>=2.12.5",
+    "pydantic-settings>=2.12.0",
+    "requests>=2.32.5",
+    "scikit-learn>=1.8.0",
+    "seaborn>=0.13.2",
+    "torch>=2.9.1",
+    "torchvision>=0.24.1",
+    "tqdm>=4.67.1",
+    "transformers>=4.57.3",
+]
+
+[tool.uv.sources]
+atkafka-consumer-python = { git = "https://tangled.org/hailey.at/atkafka-consumer-python" }

uv.lock

···
+version = 1
+revision = 3
+requires-python = ">=3.12"
+
+[[package]]
+name = "aiokafka"
+version = "0.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "async-timeout" },
+    { name = "packaging" },
+    { name = "typing-extensions" },
+]
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